Deep-Learning-Developer

Guidance for deep-learning and language-model development, including transformer models, image-generation models, and Python tools such as PyTorch, Diffusers, Transformers, and Gradio.

In plain words
What is it for?
Use it when building or reviewing training code, transformer or language models, image-generation systems, or Gradio interfaces.
Why use it?
It gives the agent context for the libraries and model types used in machine-learning projects. This can make technical suggestions better suited to deep-learning work.

Cursor rule for Cursor

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/sayeedjoy/cursor-rules/deep-learning-developer
Clone the repo
git clone --depth 1 https://github.com/sayeedjoy/cursor-rules

Made for: Cursor.

Per session 676 This file is loaded in full into every session.
When invoked 676 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00676 $0.00676
Opus 5 $0.00338 $0.00338
Sonnet 5 $0.00135 $0.00135
Haiku 4.5 $0.00068 $0.00068

Measured yesterday against content hash 108363f5a61f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Deep-Learning-Developer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/rules/python/Deep-Learning-Developer.mdc · 74 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 74 lines · 676 tokens per session scan A 108363f5a61f

Subscribe to this mod's changes

Deep-Learning-Developer is a cursor rule published in the GitHub repository sayeedjoy/cursor-rules (4 stars, last pushed 7mo ago), with no licence file. It adds 676 tokens to every session, about $0.0034 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.